BETA ScoreVariantAnnotations

Scores variant calls in a VCF file based on site-level annotations using a previously trained model

Category Variant Filtering

Overview

Scores variant calls in a VCF file based on site-level annotations using a previously trained model.

This tool is primarily intended to be used as the last step in a variant-filtering workflow that supersedes the {@link VariantRecalibrator} workflow. Using a previously trained model produced by {@link TrainVariantAnnotationsModel}, this tool assigns a score to each call (with a lower score indicating that a call is more likely to be an artifact). Each score can also be converted to a corresponding sensitivity with respect to a calibration set, if the latter is available. Each VCF record can also be annotated with additional resource labels and/or hard filtered based on its calibration-set sensitivity, if desired.

Note that annotations and metadata are collected in memory during traversal until they are written to HDF5 files upon completion of the traversal. Memory and disk requirements thus roughly scale linearly with both the number of sites scored and the number of annotations. For large callsets, this tool may be run in parallel over separate genomic shards using the "--intervals/-L" argument as usual.

Scores and annotations are also output to HDF5 files, which may be viewed using hdfview or loaded in Python using PyTables or h5py.

Inputs

Input VCF file. Site-level annotations will be extracted from the contained variants (or alleles, if at least one allele-specific annotation with "Number=A" is specified).
Annotations to use for scoring. These should be identical to those used in the {@link ExtractVariantAnnotations} step to create the training set.
Variant types (i.e., SNP and/or INDEL) to score. Logic for determining variant type was retained from {@link VariantRecalibrator}; see {@link VariantType}. To use different models for SNPs and INDELs (e.g., if it is desired to use different sets of annotations for each variant type), one can first run this tool to score SNPs and then again on the resulting output to score INDELs.
Model prefix. This should denote the path of model files produced by {@link TrainVariantAnnotationsModel}.
(Optional) Model backend. This should be identical to that specified in {@link TrainVariantAnnotationsModel}. The default Python IsolationForest implementation requires either the GATK Python environment or that certain Python packages (argparse, h5py, numpy, sklearn, and dill) are otherwise available. A custom backend can also be specified in conjunction with the "--python-script" argument.
(Optional) Resource VCF file(s). See the corresponding documentation in {@link ExtractVariantAnnotations}. In typical usage, the same resource VCFs and tags provided to that tool should also be provided here. In addition, the sites-only VCF that is produced by that tool can also be provided here and used to mark those labeled sites that were extracted, which can be useful if these are a subset of the resource sites.
(Optional) Calibration-set sensitivity thresholds for SNPs and INDELs. If the corresponding SNP or INDEL calibration-set scores are available in the provided model files, sites that have a calibration-set sensitivity falling above the corresponding threshold (i.e., a score falling below the corresponding score threshold) will have a filter applied.
Output prefix. This is used as the basename for output files.

Outputs

Scored VCF file and index. The VCF will not be gzipped if the "--do-not-gzip-vcf-output" argument is set to true. The INFO field in each VCF record will be annotated with:
1) a score (with a key as given by the "--score-key" argument, which has a default value of "SCORE"),

2) if resources are provided, flags corresponding to the labels (e.g., "training", "calibration", etc.) of resources containing the record,

3) if the "--snp-key" argument (which has a default value of "snp") is non-null, a flag corresponding to whether a site is treated as a SNP,

4) if "--snp-calibration-sensitivity-threshold" and/or "--indel-calibration-sensitivity-threshold" are provided, a filter (with name given by the "--low-score-filter-name" argument, which has a default value of "LOW_SCORE") will be applied if a record has a calibration-set sensitivity falling above the appropriate threshold (i.e., if it has a score falling below the corresponding score threshold).

In allele-specific mode (i.e., when allele-specific annotations are requested), the score, SNP flag, calibration sensitivity, and filter appropriate for the highest scoring allele are used for any multiallelic records; however, the resource labels for all alleles are applied.
(Optional) Annotations HDF5 file (.annot.hdf5). Annotation data and metadata for all scored sites (labeled and unlabeled) are stored in the HDF5 directory structure given in the documentation for the {@link ExtractVariantAnnotations} tool. This file will only be produced if the number of scored sites is nonzero.
(Optional) Scores HDF5 file (.scores.hdf5). Scores for all scored sites are stored in the HDF5 path "/data/scores". Scores are given in the same order as records in both the VCF and the annotations HDF5 file. This file will only be produced if the number of scored sites is nonzero.

Usage examples

Score sites using a model (produced by {@link TrainVariantAnnotationsModel} using the default {@link VariantAnnotationsModelBackend#PYTHON_IFOREST} model backend and contained in the directory model_dir), producing the outputs 1) output.vcf.gz, 2) output.vcf.gz.tbi, 3) output.annot.hdf5, and 4) output.scores.hdf5. Note that extract.vcf.gz is produced by {@link ExtractVariantAnnotations}. Records will be filtered according to the values provided to the "--snp-calibration-sensitivity-threshold" and "--indel-calibration-sensitivity-threshold" arguments; the values below are only meant to be illustrative and should be set as appropriate for a given analysis. Note that the "--mode" arguments are made explicit here, although both SNP and INDEL modes are selected by default.

     gatk ScoreVariantAnnotations \
          -V input.vcf \
          -A annotation_1 \
          ...
          -A annotation_N \
          --model-prefix model_dir \
          --mode SNP \
          --resource:snp-training,training=true snp-training.vcf \
          --resource:snp-calibration,calibration=true snp-calibration.vcf \
          --mode INDEL \
          --resource:indel-training,training=true indel-training.vcf \
          --resource:indel-calibration,calibration=true indel-calibration.vcf \
          --resource:extracted,extracted=true extract.vcf.gz \
          --snp-calibration-sensitivity-threshold 0.99 \
          --indel-calibration-sensitivity-threshold 0.99 \
          -O output

One may chain together two runs of this tool to score SNPs and INDELs using different models (note that SNP and INDEL models have "snp" and "indel" tags in their respective filenames, so these models can still be contained in the same model_dir directory). This may have implications for mixed SNP/INDEL sites, especially if filters are applied; see also the "--ignore-all-filters" and "--ignore-filter" arguments.

     gatk ScoreVariantAnnotations \
          -V input.vcf \
          -A snp_annotation_1 \
          ...
          -A snp_annotation_N \
          --model-prefix model_dir \
          --mode SNP \
          --resource:snp-training,training=true snp-training.vcf \
          --resource:snp-calibration,calibration=true snp-calibration.vcf \
          --resource:extracted,extracted=true snp-extract.vcf.gz \
          --snp-calibration-sensitivity-threshold 0.99 \
          -O intermediate-output

     gatk ScoreVariantAnnotations \
          -V intermediate-output.vcf.gz \
          -A indel_annotation_1 \
          ...
          -A indel_annotation_M \
          --model-prefix model_dir \
          --mode INDEL \
          --resource:indel-training,training=true indel-training.vcf \
          --resource:indel-calibration,calibration=true indel-calibration.vcf \
          --resource:extracted,extracted=true indel-extract.vcf.gz \
          --indel-calibration-sensitivity-threshold 0.99 \
          -O output

Note that separate SNP and INDEL resources are shown in the above examples purely for demonstration purposes, as are separate training and calibration resources. However, it may be desirable to specify combined resource(s); e.g., "--resource:combined-resource,training=true,calibration=true combined-resource.vcf". Recall that this is also the case in {@link ExtractVariantAnnotations}.

Custom modeling/scoring backends (ADVANCED)

The primary scoring functionality performed by this tool is accomplished by a "scoring backend" whose fundamental contract is to take an input annotation matrix and to output corresponding scores, with both input and output given as HDF5 files. Rather than using one of the available, implemented backends, advanced users may provide their own backend via the "--python-script" argument. See documentation in the modeling and scoring interfaces ({@link VariantAnnotationsModel} and {@link VariantAnnotationsScorer}, respectively), as well as the default Python IsolationForest implementation at {@link PythonVariantAnnotationsScorer} and src/main/resources/org/broadinstitute/hellbender/tools/walkers/vqsr/scalable/isolation-forest.py.

DEVELOPER NOTE: See documentation in {@link LabeledVariantAnnotationsWalker}. @author Samuel Lee <slee@broadinstitute.org>

Additional Information

Read filters

This Read Filter is automatically applied to the data by the Engine before processing by ScoreVariantAnnotations.

WellformedReadFilter

ScoreVariantAnnotations specific arguments

This table summarizes the command-line arguments that are specific to this tool. For more details on each argument, see the list further down below the table or click on an argument name to jump directly to that entry in the list.

Argument name(s)	Default value	Summary
Required Arguments
--annotation -A		Names of the annotations to extract. Note that a requested annotation may in fact not be present at any extraction site; NaN missing values will be generated for such annotations.
--model-prefix		Prefix for model files. This should be identical to the output prefix specified in TrainVariantAnnotationsModel.
--output -O		Prefix for output filenames.
--variant -V		A VCF file containing variants
Optional Tool Arguments
--arguments_file		read one or more arguments files and add them to the command line
--calibration-sensitivity-key	CALIBRATION_SENSITIVITY	Annotation key to use for calibration-sensitivity values in output.
--cloud-index-prefetch-buffer -CIPB	-1	Size of the cloud-only prefetch buffer (in MB; 0 to disable). Defaults to cloudPrefetchBuffer if unset.
--cloud-prefetch-buffer -CPB	40	Size of the cloud-only prefetch buffer (in MB; 0 to disable).
--disable-bam-index-caching -DBIC	false	If true, don't cache bam indexes, this will reduce memory requirements but may harm performance if many intervals are specified. Caching is automatically disabled if there are no intervals specified.
--disable-sequence-dictionary-validation	false	If specified, do not check the sequence dictionaries from our inputs for compatibility. Use at your own risk!
--do-not-gzip-vcf-output	false	If true, VCF output will not be compressed.
--do-not-trust-all-polymorphic	false	If true, do not trust that unfiltered records in the resources contain only polymorphic sites. This may increase runtime if the resources are not sites-only VCFs.
--double-format	%.4f	Format string to use for formatting score and calibration-sensitivity values in output.
--gcs-max-retries -gcs-retries	20	If the GCS bucket channel errors out, how many times it will attempt to re-initiate the connection
--gcs-project-for-requester-pays		Project to bill when accessing "requester pays" buckets. If unset, these buckets cannot be accessed. User must have storage.buckets.get permission on the bucket being accessed.
--help -h	false	display the help message
--ignore-all-filters	false	If true, ignore all filters in the input VCF.
--ignore-filter		Ignore the specified filter(s) in the input VCF.
--indel-calibration-sensitivity-threshold		If specified, indels with scores corresponding to a calibration sensitivity that is greater than or equal to this threshold will be hard filtered.
--interval-merging-rule -imr	ALL	Interval merging rule for abutting intervals
--intervals -L		One or more genomic intervals over which to operate
--low-score-filter-name	LOW_SCORE	Name to use for low-score filter in output.
--mode	[SNP, INDEL]	Variant types to extract.
--model-backend	PYTHON_IFOREST	Backend to use for scoring. JAVA_BGMM will use a pure Java implementation (ported from Python scikit-learn) of the Bayesian Gaussian Mixture Model. PYTHON_IFOREST will use the Python scikit-learn implementation of the IsolationForest method and will require that the corresponding Python dependencies are present in the environment. PYTHON_SCRIPT will use the script specified by the python-script argument. See the tool documentation for more details.
--omit-alleles-in-hdf5	false	If true, omit alleles in output HDF5 files in order to decrease file sizes.
--python-script		Python script used for specifying a custom scoring backend. If provided, model-backend must also be set to PYTHON_SCRIPT.
--reference -R		Reference sequence
--resource		Resource VCFs used to label extracted variants.
--resource-matching-strategy	START_POSITION	The strategy to use for determining whether an input variant is present in a resource in non-allele-specific mode. START_POSITION: Start positions of input and resource variants must match. START_POSITION_AND_GIVEN_REPRESENTATION: The intersection of the sets of input and resource alleles (in their given representations) must also be non-empty. START_POSITION_AND_MINIMAL_REPRESENTATION: The intersection of the sets of input and resource alleles (after converting alleles to their minimal representations) must also be non-empty. This argument has no effect in allele-specific mode, in which the minimal representations of the input and resource alleles must match.
--score-key	SCORE	Annotation key to use for score values in output.
--sites-only-vcf-output	false	If true, don't emit genotype fields when writing vcf file output.
--snp-calibration-sensitivity-threshold		If specified, SNPs with scores corresponding to a calibration sensitivity that is greater than or equal to this threshold will be hard filtered.
--snp-key	snp	Annotation flag to use for labeling sites as SNPs in output. Set this to "null" to omit these labels.
--version	false	display the version number for this tool
Optional Common Arguments
--add-output-sam-program-record	true	If true, adds a PG tag to created SAM/BAM/CRAM files.
--add-output-vcf-command-line	true	If true, adds a command line header line to created VCF files.
--create-output-bam-index -OBI	true	If true, create a BAM/CRAM index when writing a coordinate-sorted BAM/CRAM file.
--create-output-bam-md5 -OBM	false	If true, create a MD5 digest for any BAM/SAM/CRAM file created
--create-output-variant-index -OVI	true	If true, create a VCF index when writing a coordinate-sorted VCF file.
--create-output-variant-md5 -OVM	false	If true, create a a MD5 digest any VCF file created.
--disable-read-filter -DF		Read filters to be disabled before analysis
--disable-tool-default-read-filters	false	Disable all tool default read filters (WARNING: many tools will not function correctly without their default read filters on)
--exclude-intervals -XL		One or more genomic intervals to exclude from processing
--gatk-config-file		A configuration file to use with the GATK.
--input -I		BAM/SAM/CRAM file containing reads
--interval-exclusion-padding -ixp	0	Amount of padding (in bp) to add to each interval you are excluding.
--interval-padding -ip	0	Amount of padding (in bp) to add to each interval you are including.
--interval-set-rule -isr	UNION	Set merging approach to use for combining interval inputs
--inverted-read-filter -XRF		Inverted (with flipped acceptance/failure conditions) read filters applied before analysis (after regular read filters).
--lenient -LE	false	Lenient processing of VCF files
--max-variants-per-shard	0	If non-zero, partitions VCF output into shards, each containing up to the given number of records.
--QUIET	false	Whether to suppress job-summary info on System.err.
--read-filter -RF		Read filters to be applied before analysis
--read-index		Indices to use for the read inputs. If specified, an index must be provided for every read input and in the same order as the read inputs. If this argument is not specified, the path to the index for each input will be inferred automatically.
--read-validation-stringency -VS	SILENT	Validation stringency for all SAM/BAM/CRAM/SRA files read by this program. The default stringency value SILENT can improve performance when processing a BAM file in which variable-length data (read, qualities, tags) do not otherwise need to be decoded.
--seconds-between-progress-updates	10.0	Output traversal statistics every time this many seconds elapse
--sequence-dictionary		Use the given sequence dictionary as the master/canonical sequence dictionary. Must be a .dict file.
--tmp-dir		Temp directory to use.
--use-jdk-deflater -jdk-deflater	false	Whether to use the JdkDeflater (as opposed to IntelDeflater)
--use-jdk-inflater -jdk-inflater	false	Whether to use the JdkInflater (as opposed to IntelInflater)
--verbosity	INFO	Control verbosity of logging.
Advanced Arguments
--showHidden	false	display hidden arguments
--variant-output-filtering		Restrict the output variants to ones that match the specified intervals according to the specified matching mode.

Argument details

Arguments in this list are specific to this tool. Keep in mind that other arguments are available that are shared with other tools (e.g. command-line GATK arguments); see Inherited arguments above.

--add-output-sam-program-record / -add-output-sam-program-record

If true, adds a PG tag to created SAM/BAM/CRAM files.

boolean true

--add-output-vcf-command-line / -add-output-vcf-command-line

If true, adds a command line header line to created VCF files.

boolean true

--annotation / -A

Names of the annotations to extract. Note that a requested annotation may in fact not be present at any extraction site; NaN missing values will be generated for such annotations.

R List[String] []

--arguments_file

read one or more arguments files and add them to the command line

List[File] []

--calibration-sensitivity-key

Annotation key to use for calibration-sensitivity values in output.

String CALIBRATION_SENSITIVITY

--cloud-index-prefetch-buffer / -CIPB

Size of the cloud-only prefetch buffer (in MB; 0 to disable). Defaults to cloudPrefetchBuffer if unset.

int -1 [ [ -∞ ∞ ] ]

--cloud-prefetch-buffer / -CPB

Size of the cloud-only prefetch buffer (in MB; 0 to disable).

int 40 [ [ -∞ ∞ ] ]

--create-output-bam-index / -OBI

If true, create a BAM/CRAM index when writing a coordinate-sorted BAM/CRAM file.

boolean true

--create-output-bam-md5 / -OBM

If true, create a MD5 digest for any BAM/SAM/CRAM file created

boolean false

--create-output-variant-index / -OVI

If true, create a VCF index when writing a coordinate-sorted VCF file.

boolean true

--create-output-variant-md5 / -OVM

If true, create a a MD5 digest any VCF file created.

boolean false

--disable-bam-index-caching / -DBIC

If true, don't cache bam indexes, this will reduce memory requirements but may harm performance if many intervals are specified. Caching is automatically disabled if there are no intervals specified.

boolean false

--disable-read-filter / -DF

Read filters to be disabled before analysis

List[String] []

--disable-sequence-dictionary-validation / -disable-sequence-dictionary-validation

If specified, do not check the sequence dictionaries from our inputs for compatibility. Use at your own risk!

boolean false

--disable-tool-default-read-filters / -disable-tool-default-read-filters

Disable all tool default read filters (WARNING: many tools will not function correctly without their default read filters on)

boolean false

--do-not-gzip-vcf-output

If true, VCF output will not be compressed.

boolean false

--do-not-trust-all-polymorphic

If true, do not trust that unfiltered records in the resources contain only polymorphic sites. This may increase runtime if the resources are not sites-only VCFs.

boolean false

--double-format

Format string to use for formatting score and calibration-sensitivity values in output.

String %.4f

--exclude-intervals / -XL

One or more genomic intervals to exclude from processing
Use this argument to exclude certain parts of the genome from the analysis (like -L, but the opposite). This argument can be specified multiple times. You can use samtools-style intervals either explicitly on the command line (e.g. -XL 1 or -XL 1:100-200) or by loading in a file containing a list of intervals (e.g. -XL myFile.intervals). strings gathered from the command line -XL argument to be parsed into intervals to exclude

List[String] []

--gatk-config-file

A configuration file to use with the GATK.

String null

--gcs-max-retries / -gcs-retries

If the GCS bucket channel errors out, how many times it will attempt to re-initiate the connection

int 20 [ [ -∞ ∞ ] ]

--gcs-project-for-requester-pays

Project to bill when accessing "requester pays" buckets. If unset, these buckets cannot be accessed. User must have storage.buckets.get permission on the bucket being accessed.

String ""

--help / -h

display the help message

boolean false

--ignore-all-filters

If true, ignore all filters in the input VCF.

boolean false

--ignore-filter

Ignore the specified filter(s) in the input VCF.

List[String] []

--indel-calibration-sensitivity-threshold

If specified, indels with scores corresponding to a calibration sensitivity that is greater than or equal to this threshold will be hard filtered.

Double null

--input / -I

BAM/SAM/CRAM file containing reads

List[GATKPath] []

--interval-exclusion-padding / -ixp

Amount of padding (in bp) to add to each interval you are excluding.
Use this to add padding to the intervals specified using -XL. For example, '-XL 1:100' with a padding value of 20 would turn into '-XL 1:80-120'. This is typically used to add padding around targets when analyzing exomes.

int 0 [ [ -∞ ∞ ] ]

--interval-merging-rule / -imr

Interval merging rule for abutting intervals
By default, the program merges abutting intervals (i.e. intervals that are directly side-by-side but do not actually overlap) into a single continuous interval. However you can change this behavior if you want them to be treated as separate intervals instead.

The --interval-merging-rule argument is an enumerated type (IntervalMergingRule), which can have one of the following values:

ALL
OVERLAPPING_ONLY

IntervalMergingRule ALL

--interval-padding / -ip

Amount of padding (in bp) to add to each interval you are including.
Use this to add padding to the intervals specified using -L. For example, '-L 1:100' with a padding value of 20 would turn into '-L 1:80-120'. This is typically used to add padding around targets when analyzing exomes.

int 0 [ [ -∞ ∞ ] ]

--interval-set-rule / -isr

Set merging approach to use for combining interval inputs
By default, the program will take the UNION of all intervals specified using -L and/or -XL. However, you can change this setting for -L, for example if you want to take the INTERSECTION of the sets instead. E.g. to perform the analysis only on chromosome 1 exomes, you could specify -L exomes.intervals -L 1 --interval-set-rule INTERSECTION. However, it is not possible to modify the merging approach for intervals passed using -XL (they will always be merged using UNION). Note that if you specify both -L and -XL, the -XL interval set will be subtracted from the -L interval set.

The --interval-set-rule argument is an enumerated type (IntervalSetRule), which can have one of the following values:

UNION: Take the union of all intervals
INTERSECTION: Take the intersection of intervals (the subset that overlaps all intervals specified)

IntervalSetRule UNION

--intervals / -L

One or more genomic intervals over which to operate

List[String] []

--inverted-read-filter / -XRF

Inverted (with flipped acceptance/failure conditions) read filters applied before analysis (after regular read filters).

List[String] []

--lenient / -LE

Lenient processing of VCF files

boolean false

--low-score-filter-name

Name to use for low-score filter in output.

String LOW_SCORE

--max-variants-per-shard

If non-zero, partitions VCF output into shards, each containing up to the given number of records.

int 0 [ [ 0 ∞ ] ]

--mode

Variant types to extract.

The --mode argument is an enumerated type (List[VariantType]), which can have one of the following values:

SNP
INDEL

List[VariantType] [SNP, INDEL]

--model-backend

Backend to use for scoring. JAVA_BGMM will use a pure Java implementation (ported from Python scikit-learn) of the Bayesian Gaussian Mixture Model. PYTHON_IFOREST will use the Python scikit-learn implementation of the IsolationForest method and will require that the corresponding Python dependencies are present in the environment. PYTHON_SCRIPT will use the script specified by the python-script argument. See the tool documentation for more details.

The --model-backend argument is an enumerated type (VariantAnnotationsModelBackend), which can have one of the following values:

JAVA_BGMM
PYTHON_IFOREST: Use the script at org/broadinstitute/hellbender/tools/walkers/vqsr/scalable/isolation-forest.py
PYTHON_SCRIPT: Use a user-provided script.

VariantAnnotationsModelBackend PYTHON_IFOREST

--model-prefix

Prefix for model files. This should be identical to the output prefix specified in TrainVariantAnnotationsModel.

R String null

--omit-alleles-in-hdf5

If true, omit alleles in output HDF5 files in order to decrease file sizes.

boolean false

--output / -O

Prefix for output filenames.

R String null

--python-script

Python script used for specifying a custom scoring backend. If provided, model-backend must also be set to PYTHON_SCRIPT.

File null

--QUIET

Whether to suppress job-summary info on System.err.

Boolean false

--read-filter / -RF

Read filters to be applied before analysis

List[String] []

--read-index / -read-index

Indices to use for the read inputs. If specified, an index must be provided for every read input and in the same order as the read inputs. If this argument is not specified, the path to the index for each input will be inferred automatically.

List[GATKPath] []

--read-validation-stringency / -VS

Validation stringency for all SAM/BAM/CRAM/SRA files read by this program. The default stringency value SILENT can improve performance when processing a BAM file in which variable-length data (read, qualities, tags) do not otherwise need to be decoded.

The --read-validation-stringency argument is an enumerated type (ValidationStringency), which can have one of the following values:

STRICT
LENIENT
SILENT

ValidationStringency SILENT

--reference / -R

Reference sequence

GATKPath null

--resource

Resource VCFs used to label extracted variants.

List[FeatureInput[VariantContext]] []

--resource-matching-strategy

The strategy to use for determining whether an input variant is present in a resource in non-allele-specific mode. START_POSITION: Start positions of input and resource variants must match. START_POSITION_AND_GIVEN_REPRESENTATION: The intersection of the sets of input and resource alleles (in their given representations) must also be non-empty. START_POSITION_AND_MINIMAL_REPRESENTATION: The intersection of the sets of input and resource alleles (after converting alleles to their minimal representations) must also be non-empty. This argument has no effect in allele-specific mode, in which the minimal representations of the input and resource alleles must match.

The --resource-matching-strategy argument is an enumerated type (ResourceMatchingStrategy), which can have one of the following values:

START_POSITION
START_POSITION_AND_GIVEN_REPRESENTATION
START_POSITION_AND_MINIMAL_REPRESENTATION

ResourceMatchingStrategy START_POSITION

--score-key

Annotation key to use for score values in output.

String SCORE

--seconds-between-progress-updates / -seconds-between-progress-updates

Output traversal statistics every time this many seconds elapse

double 10.0 [ [ -∞ ∞ ] ]

--sequence-dictionary / -sequence-dictionary

Use the given sequence dictionary as the master/canonical sequence dictionary. Must be a .dict file.

GATKPath null

--showHidden / -showHidden

display hidden arguments

boolean false

--sites-only-vcf-output

If true, don't emit genotype fields when writing vcf file output.

boolean false

--snp-calibration-sensitivity-threshold

If specified, SNPs with scores corresponding to a calibration sensitivity that is greater than or equal to this threshold will be hard filtered.

Double null

--snp-key

Annotation flag to use for labeling sites as SNPs in output. Set this to "null" to omit these labels.

String snp

--tmp-dir

Temp directory to use.

GATKPath null

--use-jdk-deflater / -jdk-deflater

Whether to use the JdkDeflater (as opposed to IntelDeflater)

boolean false

--use-jdk-inflater / -jdk-inflater

Whether to use the JdkInflater (as opposed to IntelInflater)

boolean false

--variant / -V

A VCF file containing variants

R GATKPath null

--variant-output-filtering

Restrict the output variants to ones that match the specified intervals according to the specified matching mode.

The --variant-output-filtering argument is an enumerated type (Mode), which can have one of the following values:

STARTS_IN: starts within any of the given intervals
ENDS_IN: ends within any of the given intervals
OVERLAPS: overlaps any of the given intervals
CONTAINED: contained completely within a contiguous block of intervals without overlap
ANYWHERE: no filtering

Mode null

--verbosity / -verbosity

Control verbosity of logging.

The --verbosity argument is an enumerated type (LogLevel), which can have one of the following values:

ERROR
WARNING
INFO
DEBUG

LogLevel INFO

--version

display the version number for this tool

boolean false

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GATK version 4.6.2.0 built at Sun, 13 Apr 2025 13:21:43 -0400.

**BETA** ScoreVariantAnnotations

Category Variant Filtering

Overview

Inputs

Outputs

Usage examples

Custom modeling/scoring backends (ADVANCED)

Additional Information

Read filters

ScoreVariantAnnotations specific arguments

Argument details

--add-output-sam-program-record / -add-output-sam-program-record

--add-output-vcf-command-line / -add-output-vcf-command-line

--annotation / -A

--cloud-index-prefetch-buffer / -CIPB

--cloud-prefetch-buffer / -CPB

--create-output-bam-index / -OBI

--create-output-bam-md5 / -OBM

--create-output-variant-index / -OVI

--create-output-variant-md5 / -OVM

--disable-bam-index-caching / -DBIC

--disable-read-filter / -DF

--disable-sequence-dictionary-validation / -disable-sequence-dictionary-validation

--disable-tool-default-read-filters / -disable-tool-default-read-filters

--exclude-intervals / -XL

--gcs-max-retries / -gcs-retries

--help / -h

--input / -I

--interval-exclusion-padding / -ixp

--interval-merging-rule / -imr

--interval-padding / -ip

--interval-set-rule / -isr

--intervals / -L

--inverted-read-filter / -XRF

--lenient / -LE

--output / -O

--read-filter / -RF

--read-index / -read-index

--read-validation-stringency / -VS

--reference / -R

--seconds-between-progress-updates / -seconds-between-progress-updates

--sequence-dictionary / -sequence-dictionary

--showHidden / -showHidden

--use-jdk-deflater / -jdk-deflater

--use-jdk-inflater / -jdk-inflater

--variant / -V

--verbosity / -verbosity

BETA ScoreVariantAnnotations